Bibliographic record
Abstract
Are we starting to see the jobs of the future through the COVID-19 pandemic? This paper argues that in a few industries such as agriculture, healthcare, education, and mental health, we are starting to see jobs that we didn’t fully imagine a few years ago. Online appointments can increase attendance of reluctant mental health patients, such as military veterans. Conferences may have a remote option so that more people could attend, particularly from other countries. Agricultural workers could access on-site healthcare as needed, or the remaining workers, after automation, could get immediate medical help in the case of an injury. Providing protective equipment, its manufacture and distribution, could be part of a new higher standard of hygiene in providing oral care. While all the changes will not be permanent, the pandemic’s duration is giving us an idea of how the workplace will change. The pandemic has increased the role telecommunications and information technology play in an economy that had already come to rely on both to fuel a workstation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".